The development of fuel cell electric vehicles in recent years has led to increased interest in the use of fuel cells as sources of renewable energy. To achieve successful commercialization of fuel cell vehicles, it will be necessary to guarantee the safety, reliability, and lifetime of fuel cell systems by predictive fault detection and isolation (FDI). In this study, the parity equation, an observer, and a Kalman filter are employed together to compare the characteristics of FDI, focusing on the sensors of the thermal management system. Residuals corresponding to the difference between temperature outputs of linear models under driving cycles and nonlinear temperature outputs are used to isolate faults. Then, assessment of three model-based sensor FDI schemes is used to isolate sensor faults using the Cumulative Sum Control Chart (CUSUM) method. Generated residuals are evaluated by CUSUM to detect the presence of a sensor fault. As a result, isolated sensor faults are assessed.

Proton Exchange Membrane Fuel Cell (PEMFC) has advantages that other energy sources don’t have, and recently, it has been spotlighted in many industries such as transportation and power generation. However, although much research has been conducted on PEMFC, studies on operating conditions of bipolar plates in cells have been insufficient. Most of the studies that have been